Policy Frameworks

Risk-based classification of AI systems. High-risk systems (biometrics, employment, education, critical infrastructure) must meet transparency, human oversight, and risk assessment requirements. Third-party audits every 2 years. Full applicability August 2, 2026.

Entered into force August 2024. GPAI model obligations applied August 2025. High-risk AI rules apply August 2026. Extended transition for embedded high-risk systems to August 2027. First comprehensive binding AI regulation globally.

Seven Pillars: protecting children, communities, creators, and free speech; maintaining US AI dominance; federal preemption of state AI laws; innovation-first; regulatory sandboxes. Calls on Congress to establish a single federal AI standard.

Non-binding framework released March 20, 2026. Follows December 2025 EO. Congressional implementation uncertain. Contrasts with EU rights-based approach by prioritizing innovation and US competitiveness.

Promoting US AI dominance; preventing state-level regulatory fragmentation; removing ideological constraints on AI development; national AI Action Plan.

Replaced EO 14110. AI Action Plan published July 2025.

Sector-specific standards; AI Safety Board (pilot); data provenance; model ethics; sandbox-to-regulation model; aligns with OECD and G20 AI Principles.

India follows 'sandbox-to-regulation' approach; high public trust in AI (77%).

Six areas: infrastructure, data R&D, talent, governance, investment, ethics/equity/inclusion. Responsible AI deployment; data privacy; ethical AI governance.

One of the most developed AI governance frameworks in Sub-Saharan Africa.

Risk-based classification similar to EU AI Act; lighter compliance burden; emphasis on promoting AI innovation; AI safety committees; transparency obligations.

First binding national AI law in East Asia outside China.

First international standards protecting 'mental privacy' and thought autonomy as AI-enabled brain-computer interfaces and neural data collection become commercially viable. Extends AI ethics framework to neurotechnology domain.

Adopted late 2025. Addresses risks of AI-enhanced neurotechnology. Part of broader UNESCO AI ethics architecture alongside 2021 Recommendation on the Ethics of AI.

Risk-based governance; updated IP and cybersecurity provisions; AI-specific incident response. Phased four-year implementation.

First country in Southeast Asia to enact a formal AI law.

Harnessing AI benefits; building capabilities; minimizing risks; stimulating investment; fostering cooperation. Targets agriculture, healthcare, education, climate. 15 policy action points.

Aligned with UNESCO RAM and Ethical Impact Assessment. Phase 1 (2025–2026) focuses on governance structures.

Risk-based governance; human-centricity; transparency; fairness; security; accountability. Aligned with OECD AI Principles.

Most ASEAN member AI frameworks reference this guide. Vietnam enacted binding AI Law in Dec 2025.

Risk-based approach; horizontal framework; human rights focus; transparency; accountability; alignment with OECD and UNESCO frameworks.

Among first Latin American countries pursuing comprehensive AI law.

Human rights, democracy, rule of law in AI contexts. First legally binding international AI treaty. Focuses on AI used by public authorities and private actors on their behalf.

First legally binding international AI governance instrument.

Risk-based tiered approach: Unacceptable (banned), High-risk, Limited-risk, Minimal-risk. Covers GPAI models, transparency, human oversight, conformity assessments, AI Office oversight.

World's first comprehensive binding AI law. High-risk rules enter force Aug 2026.

Critical capability levels (CCLs); evaluations for dangerous capabilities (e.g., CBRN, cyberoffense); security measures at each level; internal safety review.

Part of the 'big three' frontier lab safety frameworks alongside Anthropic and OpenAI.

Ethical AI; human rights; inclusive growth; AI readiness based on UNESCO RAM. Ranks 42nd globally and top in Africa per UNESCO assessment.

Led by UNESCO RAM assessment; linked to Africa Declaration on AI (Kigali, 2025).

AI Safety Levels (ASL); commitments on capability thresholds before deployment; safety research requirements; model evaluations for dangerous capabilities; transparency via public releases.

Pioneered 'responsible scaling' model; referenced by other frontier AI labs.

Frontier AI safety; catastrophic and systemic AI risks; international cooperation on AI safety testing; establishment of national AI safety institutes.

Led to creation of AI Safety Institutes in UK, USA, and other nations.

Accountability, safety, fairness, transparency, human oversight, data governance for generative AI. Signatories include major Canadian and international tech firms.

Replaced failed Artificial Intelligence and Data Act (AIDA). Aligns with OECD and GPAI.

Governs synthetic media (deepfakes, voice cloning, AI-generated images/video). Mandatory labelling, user registration, content monitoring, fraud prevention.

China's first generative AI regulation; focused on misinformation and social harm.

Content moderation; training data requirements; labelling of AI-generated content; data protection; algorithm filing for public-facing services; 'core socialist values' alignment.

World's first binding generative AI regulation. Part of a layered regulatory stack.

AI safety testing & red-teaming for frontier models; standards for watermarking; workforce guidance; civil rights protections; international leadership.

Revoked by Executive Order 14179 (Jan 2025) which shifted to innovation-led approach.

Pre-deployment safety testing; incident information sharing; AI watermarking; investment in AI safety research; transparency to users; responsible disclosure of vulnerabilities.

Non-binding but signals convergence among G7 on frontier AI governance. Expands annually.

Risk management lifecycle for AI systems; hazard identification; risk estimation and evaluation; risk treatment; aligned with ISO 31000 and ISO/IEC 42001.

Companion standard to ISO 42001; cited in NIST AI RMF crosswalks.

Plan-Do-Check-Act management system for AI. Covers leadership, risk management, data governance, lifecycle controls, transparency, accountability, continuous improvement.

First certifiable global AI governance standard. Complements NIST AI RMF and EU AI Act.

Four functions: GOVERN, MAP, MEASURE, MANAGE. Covers trustworthiness, safety, fairness, explainability, privacy, accountability throughout the AI lifecycle.

De facto standard for US organizations; crosswalked to ISO 42001, EU AI Act, and OECD Principles.

Four risk categories: cybersecurity, CBRN, persuasion, model autonomy. Risk thresholds (low/medium/high/critical) govern deployment decisions. Safety Advisory Group review.

Closely mirrors Anthropic RSP; part of broader frontier model safety movement.

Cross-sector principles: safety, security, fairness, accountability, transparency, contestability, redress. Sector regulators apply to existing domains. AI Safety Institute conducts frontier model evaluations.

UK chose sector-specific approach rather than horizontal legislation. AI Safety Institute (AISI) evaluates frontier models.

Human oversight; international cooperation on AI safety; ban on autonomous weapons; independent AI scientific panel; AI for sustainable development.

Led to the first UN Global Dialogue on AI Governance in 2026.

Transparency of recommendation algorithms; prohibition on manipulative practices; algorithm filing with CAC; adherence to 'mainstream values'; user opt-out rights.

Covers social media, e-commerce, and news platforms.

Algorithmic transparency for platforms; accountability for online intermediaries; trustworthy data sharing frameworks; obligations for very large online platforms.

Complements the AI Act for platform-based AI accountability.

International cooperation; voluntary industry self-regulation; AI safety research; alignment with G7 Hiroshima Process. Human-centric AI principles.

Japan favors voluntary cooperation over binding legislation.

Six principles: fairness, reliability & safety, privacy & security, inclusiveness, transparency, accountability. Sensitive use review process; AI impact assessments.

Widely referenced by enterprises building on Azure AI. Accompanied by Responsible AI Tools.

Five principles: safe & effective systems; algorithmic discrimination protections; data privacy; notice & explanation; human alternatives & fallback.

Not legally enforceable but guides federal agency AI policy.

Value-based design process for technology systems. IEEE 7000 covers ethical considerations in system design; related standards cover algorithmic bias (7003), transparency (7001), and children's data (7004).

Standards-based design process; applied to any system type integrating AI.

Human dignity, human rights, fairness, non-discrimination, sustainability, privacy, safety. Includes Readiness Assessment Methodology (RAM) and Ethical Impact Assessment (EIA). 11 policy action areas.

First global AI ethics instrument adopted by all UNESCO member states. RAM piloted in 60+ countries.

Responsible AI; data governance; future of work; innovation & commercialization. Implements OECD AI Principles via working groups and expert research.

Merged with the OECD AI Policy Observatory in 2024 to consolidate resources.

Seven key requirements: human agency & oversight; technical robustness; privacy & data governance; transparency; diversity & fairness; societal well-being; accountability.

Precursor to the EU AI Act; widely referenced internationally.

Inclusive growth; human-centered values; transparency & explainability; robustness, security & safety; accountability. Widely used as reference for national legislation.

Most widely endorsed international AI framework; tracked via OECD AI Policy Observatory across 1,000+ national initiatives.

Internal governance; human oversight; operations management; stakeholder interaction. 2026 edition adds Agent Identity Cards, graduated autonomy levels (0–4), and multi-agent accountability.

World's first agentic AI governance framework released Jan 2026. Also offers AI Verify testing toolkit.

Ten principles: well-being, autonomy, justice, privacy, knowledge, democracy, responsibility, sustainability, digital divide, and prudence.

Developed through broad public consultation in Quebec; influential in North American policy circles.

Notice to affected users; meaningful appeal processes; transparency reporting on AI-driven content moderation and enforcement.

Increasingly applied to AI moderation contexts.

23 principles across research issues, ethics, and long-term issues including AI arms race avoidance, recursive self-improvement limits, and common good orientation. Signed by 1,000+ AI researchers.

Early influential framework; forerunner to modern frontier AI safety governance.

Safety; transparency and explainability; fairness and absence of bias; accountability; privacy; societal benefit. Six governance priorities issued for 2026.

Members include Anthropic, Google, Meta, Microsoft, Apple, Amazon, and hundreds of civil society orgs.